Explore Workflows

RNA-Seq strand specific mitochondrial workflow for single-read experiment based on BioWardrobe's basic analysis.

https://github.com/datirium/workflows.git

Path: workflows/rnaseq-se-dutp-mitochondrial.cwl

Branch/Commit ID: cf107bc24a37883ef01b959fd89c19456aaecc02

Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_indexbuilder.cwl

Branch/Commit ID: 60fafdfbec9b39c860945ef4634e0c28cb5e976c

Workflow for quality assessment of paired reads and classification using NGTax 2.0. In addition files are exported to their respective subfolders for easier data management in a later stage. Steps: - FastQC (read quality control) - NGTax 2.0 - Export module

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_ngtax.cwl

Branch/Commit ID: 60fafdfbec9b39c860945ef4634e0c28cb5e976c

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/facets.cwl

Branch/Commit ID: e96613177b18b76c6fac98b945660bde65ebdd80

https://github.com/genome/cancer-genomics-workflow.git

Path: unaligned_bam_to_bqsr/workflow_no_dup_marking.cwl

Branch/Commit ID: eb565eac07209017b12ed79057b40cbf44fb6a0d

### Pipeline for Lexogen's QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina [Lexogen original documentation](https://www.lexogen.com/quantseq-3mrna-sequencing/) * Cost-saving and streamlined globin mRNA depletion during QuantSeq library preparation * Genome-wide analysis of gene expression * Cost-efficient alternative to microarrays and standard RNA-Seq * Down to 100 pg total RNA input * Applicable for low quality and FFPE samples * Single-read sequencing of up to 9,216 samples/lane * Dual indexing and Unique Molecular Identifiers (UMIs) are available ### QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina The QuantSeq FWD Kit is a library preparation protocol designed to generate Illumina compatible libraries of sequences close to the 3’ end of polyadenylated RNA. QuantSeq FWD contains the Illumina Read 1 linker sequence in the second strand synthesis primer, hence NGS reads are generated towards the poly(A) tail, directly reflecting the mRNA sequence (see workflow). This version is the recommended standard for gene expression analysis. Lexogen furthermore provides a high-throughput version with optional dual indexing (i5 and i7 indices) allowing up to 9,216 samples to be multiplexed in one lane. #### Analysis of Low Input and Low Quality Samples The required input amount of total RNA is as low as 100 pg. QuantSeq is suitable to reproducibly generate libraries from low quality RNA, including FFPE samples. See Fig.1 and 2 for a comparison of two different RNA qualities (FFPE and fresh frozen cryo-block) of the same sample.  Figure 1 | Correlation of gene counts of FFPE and cryo samples.  Figure 2 | Venn diagrams of genes detected by QuantSeq at a uniform read depth of 2.5 M reads in FFPE and cryo samples with 1, 5, and 10 reads/gene thresholds. #### Mapping of Transcript End Sites By using longer reads QuantSeq FWD allows to exactly pinpoint the 3’ end of poly(A) RNA (see Fig. 3) and therefore obtain accurate information about the 3’ UTR.  Figure 3 | QuantSeq read coverage versus normalized transcript length of NGS libraries derived from FFPE-RNA (blue) and cryo-preserved RNA (red). ### Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Separates UMIes and trims adapters from input FASTQ file 2. Uses ```STAR``` to align reads from input FASTQ file according to the predefined reference indices; generates unsorted BAM file and alignment statistics file 3. Uses ```fastx_quality_stats``` to analyze input FASTQ file and generates quality statistics file 4. Uses ```samtools sort``` and generates coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 2 (after running STAR) 5. Uses ```umi_tools dedup``` and generates final filtered sorted BAM(+BAI) file pair 6. Generates BigWig file on the base of sorted BAM file 7. Maps input FASTQ file to predefined rRNA reference indices using ```bowtie``` to define the level of rRNA contamination; exports resulted statistics to file 8. Calculates isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; exports results to file

https://github.com/datirium/workflows.git

Path: workflows/trim-quantseq-mrnaseq-se.cwl

Branch/Commit ID: 60854b5d299df91e135e05d02f4be61f6a310fbc

SAMSA2 complete workflow for meta-omics read annotation Steps: - Diamond read blastx - Refseq - SEED - SAMSA2 processing

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_samsa2.cwl

Branch/Commit ID: 60fafdfbec9b39c860945ef4634e0c28cb5e976c

Reverse the lines in a document, then sort those lines.

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: a3d565bf8e630101d25d31804cfbceb0a0ba28de

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

https://github.com/datirium/workflows.git

Path: workflows/deseq.cwl

Branch/Commit ID: 799575ce58746813f066a665adeacdda252d8cab

https://github.com/common-workflow-language/common-workflow-language.git

Path: v1.0/v1.0/count-lines8-wf-noET.cwl

Branch/Commit ID: 1f501e38ff692a408e16b246ac7d64d32f0822c2